Papers with sentiment
A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifiers (2024.emnlp-main)
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| Challenge: | Recent research shows that named entities influence PLMs in many applications. |
| Approach: | They propose a method to quantify biases associated with named entities from various countries using Twitter data instead of templates or specific datasets. |
| Outcome: | The proposed method shows positive biases related to the language spoken in a country across all classifiers. |
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews (D19-55)
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| Challenge: | sarcasm, humor, hate speech, and sentiment are a complex language attribute . sentiment classification models are used for complex language understanding tasks . |
| Approach: | They propose a two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment from online reviews and feeds them to inform sentiment classification. |
| Outcome: | The proposed model improves on sarcasm, humor, hate speech and sentiment classification . it can be combined with other models to achieve similar results . |
Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)
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| Challenge: | Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. |
| Approach: | They propose a method for fine-tuning pretrained language models on automatically generated paraphrase data to improve the efficiency of style transfer. |
| Outcome: | The proposed method outperforms state-of-the-art style transfer systems on human and automatic evaluations and proposes fixed variants. |
Mixture of Multimodal Adapters for Sentiment Analysis (2025.naacl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used for text sentiment analysis but sentiment is hidden in other modalities. |
| Approach: | They propose to fuse emotions from different data to analyze sentiments . they use compression parameter for each expert to reduce training burden . |
| Outcome: | The proposed method achieves state-of-the-art with a tiny trainable parameter count compared to current methods . emotions hidden in body movements or vocal timbres eclipse traditional methods compared with text sentiment analysis . |
A Recipe for Arbitrary Text Style Transfer with Large Language Models (2022.acl-short)
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| Challenge: | augmented zero-shot learning is a prompting method that allows large language models to perform zero-shoot text style transfer to arbitrary styles, without any model fine-tuning or exemplars in the target style. |
| Approach: | They propose a prompting method that frames style transfer as a sentence rewriting task and requires only a natural language instruction. |
| Outcome: | The proposed method is based on a large language model and is shown to perform on standard style transfer tasks and arbitrary transformations. |
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets (2020.emnlp-main)
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| Challenge: | Social media sites have the potential to complement surveys that measure political opinions and, more specifically, political actors’ approval. |
| Approach: | They propose to compare untargeted sentiment, targeted sentiment, and stance detection methods to a set of custom models trained on minimal custom data. |
| Outcome: | The proposed methods have low generalizability on unseen and familiar targets, while low-resource custom models are more robust. |
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes (2022.lrec-1)
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| Challenge: | Recent studies on sentiment analysis of memes have focused on English, but there is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. |
| Approach: | They propose to use a Bengali dataset to perform multimodal sentiment analysis in low resource languages. |
| Outcome: | The proposed dataset for Bengali contains 4417 memes with three annotated labels positive, negative, and neutral. |
On the Interplay Between Fine-tuning and Composition in Transformers (2021.findings-acl)
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| Challenge: | Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks. |
| Approach: | They propose to fine-tune transformer language models on a paraphrase and sentiment task and analyze their results to determine whether they benefit compositionality. |
| Outcome: | The proposed model performance on a paraphrase and sentiment task is compared with pre-trained models on lexical-level representations. |
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages (P18-1)
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| Challenge: | Existing approaches to sentiment analysis in low-resource languages lack annotated corpora or do not capture sentiment information. |
| Approach: | They propose a model that represents sentiment in a source and target language without annotated corpus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four out of six setups and captures complementary information to machine translation. |
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
| Approach: | They propose to fine-tune pre-trained language models to optimize the zero-shot learning objective by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering format. |
| Outcome: | The proposed model outperforms a same-sized QA model and the previous SOTA zero-shot learning system on unseen tasks. |
Latent Variable Sentiment Grammar (P19-1)
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| Challenge: | Existing neural models do not explicitly model sentiment composition, which requires to encode sentiment class labels. |
| Approach: | They propose a sentiment grammar that captures sentiment subtype expressions by latent variables and Gaussian mixture vectors. |
| Outcome: | The proposed model outperforms vanilla neural encoders on the Stanford Sentiment Treebank benchmark. |
Evaluating Word Expansion for Multilingual Sentiment Analysis of Parliamentary Speech (2024.lrec-main)
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| Challenge: | Recent efforts to create and format data sets of parliamentary speech material have facilitated cross-lingual comparisons and highlighted the need for methods that are computationally efficient and language-agnostic. |
| Approach: | They propose a word expansion method for sentiment lexicon generation that leverages word embeddings and vector similarity to expand synonym seed lists with domain-specific terms from the speech corpora. |
| Outcome: | The proposed method is compared with other multilingual lexica and is highly sensitive to processing and scoring techniques. |
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard (L18-1)
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| Challenge: | standardized dialog act corpora are used for conversation mining research . different corporations often use different methods to understand interaction structure . |
| Approach: | They propose to annotate dialog acts using ISO 24617-2 standard (2012) . they also annotated emotions using Ekman's six primitives and sentiment using tags "positive", "negative" and "neutral" |
| Outcome: | The proposed corpus is constructed using the ISO 24617-2 standard (2012) . it is used for emotions, sentiment and positive, negative and neutral tags . |
Diffusion Guided Language Modeling (2024.findings-acl)
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| Challenge: | Existing guidance methods for text generation are prone to decoding errors and degrade performance. |
| Approach: | They propose a model that steers an auto-regressive language model to generate text with desired properties. |
| Outcome: | The proposed model outperforms existing guidance methods on a wide range of benchmark data sets. |
Elevating Code-mixed Text Handling through Auditory Information of Words (2023.emnlp-main)
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| Challenge: | Current language models focus on the semantic representation of words and ignore the auditory phonetic features. |
| Approach: | They propose an approach to create language models for handling code-mixed textual data using auditory phonetic features from SOUNDEX using auditorian information. |
| Outcome: | The proposed approach improves robustness against adversarial attacks on code-mixed classification tasks and improves classification results over baselines. |
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings (2024.lrec-main)
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| Challenge: | The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. |
| Approach: | They propose to use a dataset of sentences manually annotated for sentiment to train a robust sentiment identifier for parliamentary proceedings. |
| Outcome: | The proposed model performs very well on languages not seen during fine-tuning and additional fine- tuning data from other languages significantly improves the target parliament’s results. |
SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning (2026.acl-long)
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Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, Dani Bouch, Momina Ahsan, Muhra AlMahri, Marwa Elsaid Khalil, Yuxia Wang, Salem Lahlou, Sophia Ananiadou, Veselin Stoyanov, Jimin Huang, Xueqing Peng, Preslav Nakov, Zhuohan Xie
| Challenge: | English financial NLP has progressed rapidly through benchmarks for sentiment, document understanding, and financial question answering. |
| Approach: | They propose a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning. |
| Outcome: | The proposed dataset contains 14,380 expert-verified instances spanning seven tasks . it includes financial sentiment analysis, extractive summarization, and event–cause reasoning . |